# The Value of Information in Resource-Constrained Pricing

arXiv: https://arxiv.org/abs/2603.24974 · HTML: https://mlsanity.com/paper/2603.24974/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 17 of 17)

- [Upper Counterfactual Confidence Bounds: a New Optimism Principle for Contextual Bandits](https://mlsanity.com/paper/2007.07876/) (2020)
- [Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints](https://mlsanity.com/paper/2504.11320/) (2025)
- [Online Linear Optimization with Many Hints](https://mlsanity.com/paper/2010.03082/) (2020)
- [Large-Scale Optimization Model Auto-Formulation: Harnessing LLM Flexibility via Structured Workflow](https://mlsanity.com/paper/2601.09635/) (2026)
- [Logarithmic Regret from Sublinear Hints](https://mlsanity.com/paper/2111.05257/) (2021)
- [Online Contextual Decision-Making with a Smart Predict-then-Optimize Method](https://mlsanity.com/paper/2206.07316/) (2022)
- [Online Bandits with (Biased) Offline Data: Adaptive Learning under Distribution Mismatch](https://mlsanity.com/paper/2405.02594/) (2024)
- [Best Arm Identification with LLM Judges and Limited Human](https://mlsanity.com/paper/2601.21471/) (2026)
- [AI Agents for Inventory Control: Human-LLM-OR Complementarity](https://mlsanity.com/paper/2602.12631/) (2026)
- [Ask, Clarify, Optimize: Human-LLM Agent Collaboration for Smarter Inventory Control](https://mlsanity.com/paper/2601.00121/) (2025)
- [OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents](https://mlsanity.com/paper/2602.19439/) (2026)
- [PPI-SVRG: Unifying Prediction-Powered Inference and Variance Reduction for Semi-Supervised Optimization](https://mlsanity.com/paper/2601.21470/) (2026)
- [LLM-SAA: LLM-persona Generated Distributions for Decision-making](https://mlsanity.com/paper/2602.06357/) (2026)
- [Online Resource Allocation with Average Budget Constraints](https://mlsanity.com/paper/2402.11425/) (2024)
- [Two-stage Online Reusable Resource Allocation: Reservation, Overbooking and Confirmation Call](https://mlsanity.com/paper/2410.15245/) (2024)
- [ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction and Behavioral Rationality in Operations Research](https://mlsanity.com/paper/2601.21008/) (2026)
- [Designing Service Systems from Textual Evidence](https://mlsanity.com/paper/2603.10400/) (2026)

## Abstract (arXiv)

Firms that price perishable resources -- airline seats, hotel rooms, seasonal inventory -- now routinely use demand predictions, but these predictions vary widely in quality. Under hard capacity constraints, acting on an inaccurate prediction can irreversibly deplete inventory needed for future periods. We study how prediction uncertainty propagates into dynamic pricing decisions with linear demand, stochastic noise, and finite capacity. A certified demand forecast with known error bound~ε0 specifies where the system should operate: it shifts regret from O(√T) to O(log T) when ε0 lesssim T-1/4, and we prove this threshold is tight. A misspecified surrogate model -- biased but correlated with true demand -- cannot set prices directly but reduces learning variance by a factor of (1-ρ2) through control variates. The two mechanisms compose: the forecast determines the regret regime; the surrogate tightens estimation within it. All algorithms rest on a boundary attraction mechanism that stabilizes pricing near degenerate capacity boundaries without requiring non-degeneracy assumptions. Experiments confirm the phase transition threshold, the variance reduction from surrogates, and robustness across problem instances.